A key challenge associated with cellular-based machine-type communication (MTC) is to provide better coverage enhancement (CE) along with low power consumption for MTC user equipments (UEs). Due to the limitation on total power allowed for transmission, it is required to estimate frequency error in very low signal-to-noise ratio condition. In this article, an effective frequency offset estimation method is presented for MTC in a long term evolution system. The proposed method applies the repetitive nature of the MTC physical broadcast channel to the weighted least-squares estimation technique. Both analytical and simulation results are provided to corroborate the feasibility of the proposed method. It is shown that the proposed estimation scheme is more efficient for CE of MTC UEs, compared to the existing method.

Quantizers are generally designed either for fixed-rate coding or variable-rate coding. Under common conditions, variable-rate quantization leads to the same mean distortion for all quantization cells whereas fixed-rate is associated with a fixed bit allocation for each quantization operation. We show that both are special cases of the solution of a more general constraint that facilitates a practical trade-off between mean rate and rate outliers and mean distortion and outliers in distortion. The proposed constraint weights the code-word lengths exponentially. The resulting quantizers are suitable for commonly occurring scenarios such as those where a limited variability in rate can be tolerated by the network, those where the signal model may not be accurate, and those where source redundancy is used to counter channel errors.

For quantization of line spectral frequency (LSF), Gaussian mixture model (GMM) based switched split vector quantization (SSVQ) has been reported as the best performing intra-frame coding method. However, GMM-SSVQ partly recovers correlations between the subvectors of split vector quantization (SVQ). In the proposed GMM-SSVQ with the Karhunen-Loève Transform (KLT), KLT-domain quantization for each mixture with a novel region-clustering algorithm is applied to GMM-SSVQ. Compared with SVQ and GMM-SSVQ, it provides 4 and 1 bit higher performance in terms of average spectral distortion and outliers, respectively. Computational complexity and memory requirements are similar to GMM-SSVQ.

The rate-distortion performance of conventional resolution-constrained quantization (RCQ) based on the mean-squared error criterion (MSE-RCQ) is generally compromised by the impact of distortion outliers and source mismatch. Not only the mean distortion, but also the number of distortion outliers should be considered in quantizer design. Thus, we propose the use of a design criterion that gives more importance to the tail of the source distribution, which leads to RCQ based on the second moment of distortion (SMD-RCQ). A continuous range of alternatives between MSE-RCQ and SMD-RCQ is also defined and implemented based on the weighted arithmetic-mean measure (WAM-RCQ). It can be used to control the centroid density in the tail of the source distribution. Experimental results with a Gaussian source and line spectral frequencies (LSFs) show that the proposed WAM-RCQ not only produces a similar mean distortion as conventional MSE-RCQ, but has a lower percentage of distortion outliers and a significantly reduced sensitivity to source mismatch.

It is well known that usage of an appropriate representation of the speech signal improves the performance of speech coders, recognizers, and synthesizers. In this paper we present a representation of speech that has the efficiency, in terms of being compact, similar to that of parametric modeling, but additionally has the completeness property of signal expansions. The resulting canonical representation of speech is suited for a wide range of speech processing applications and we demonstrate this through experiments related to coding and prosodic modification.

When the squared error of observable signal parameters is below the just noticeable difference (JND), it is not registered by human perception. We modify commonly used distortion criteria to account for this phenomenon and study the implications for quantizer design and performance. Taking the JND into account in the design of the quantizer generally leads to improved performance in terms of mean distortion and the number of outliers. Moreover, the resulting quantizer exhibits better robustness against source mismatch

To facilitate real-time audiovisual communication through the Internet, forward error correction (FEC) and multiple description coding (MDC) can be used as low-delay packet-loss recovery techniques. We use both a Gilbert channel model and data obtained from real IP connections to compare the rate-distortion performance of different variants of FEC and MDC. Using identical overall rates with stringent delay constraints, we find that side-distortion optimized MDC generally performs better than Reed-Solomon-based FEC. If the channel condition is known from feedback, then channel-optimized MDC can be used to exploit this information, resulting in significantly improved performance. Our results confirm that two-independent-channel transmission is preferred to single-channel transmission, both for FEC and MDC.

To facilitate real-time voice communication through the Internet, forward error correction (FEC) and multiple description coding (MDC) can be used as low-delay packet-loss recovery techniques. We u ...

Compared to scalar quantization (SQ), vector quantization (VQ) has memory, space-filling, and shape advantages. If the signal statistics are known, direct vector quantization (DVQ) according to these statistics provides the highest coding efficiency, but requires unmanageable storage requirements if the statistics are time varying. In code-excited linear predictive (CELP) coding, a single "compromise" codebook is trained in the excitation-domain and the space-filling and shape advantages of VQ are utilized in a nonoptimal, average sense. In this paper, we propose Karhunen-Loe/spl grave/ve transform (KLT)-based adaptive classified VQ (CVQ), where the space-filling advantage can be utilized since the Voronoi-region shape is not affected by the KLT. The memory and shape advantages can be also used, since each codebook is designed based on a narrow class of KLT-domain statistics. We further improve basic KLT-CVQ with companding. The companding utilizes the shape advantage of VQ more efficiently. Our experiments show that KLT-CVQ provides a higher SNR than basic CELP coding, and has a computational complexity similar to DVQ and much lower than CELP. With companding, even single-class KLT-CVQ outperforms CELP, both in terms of SNR and codebook search complexity.

If the signal statistics are given, direct vector quantization (DVQ) according to these statistics provides the highest coding efficiency, but requires unmanageable storage requirements. In. code-excited linear predictive (CELP) coding. a single “compromise” codebook is trained in the prediction residual-domain and the space-filling and shape advantages of vector quantization (VQ) are utilized in a non-optimal, average sense. In this paper. we propose a Karhunen-Loève Transform (KLT)-based classified VQ (CVQ), where the space-filling advantage can be utilized since the Voronoi-region shape is not affected by the KLT. The memory and shape advantages can be also used, since each codebook is designed based on a narrow class of KL T -domain statistics. Our experiments show that the KLT-CVQ provides a higher SNR than CELP and (single-codebook) DVQ, and has a computational complexity similar to DVQ and much lower than CELP. Storage requirements are modest because of the energy concentration property of the KL T.